在過去幾年間影像超解析度方法的研究越來越普遍，影像超解析度方法的應用也越來越廣泛，不論是智慧型手機、電視、網路攝影鏡頭、數位相機...等等都有應用影像超解析度方法的部分。影像超解析度方法是將一張低解析度影像，經過各種不同的方法，以獲得其高解析度影像的演算法。
本篇論文中，我們提出一個基於線性局部插入法、不需要用到額外的影像資料庫，只需要單張影像的影像超解析度方法，這個方法並將夏普利值應用於其中。夏普利值常在聯盟賽局裡被用來分配參與者的報酬，夏普利值是藉由計算出賽局裡每個參與者貢獻來決定如何分配報酬。
最後我們經由實驗證明，本論文的演算法所產出的高解析度影像無論是在主觀感覺或是客觀標準的影像品質上都有不錯的結果。

Super-resolution is very popular for research in the image processing during past years. It has been widely used on different digital devices such as smart phones, televisions, webcams and digital cameras. The goal of super resolution is to get a high resolution image from one or more low resolution images.
In this thesis, we propose a super resolution method that generates a high-resolution image from a single low resolution image without training images by local linear embedding. We use the input low resolution image itself and its down-sample image as training images. In our algorithm, we find several nearest patches for each input low resolution image patch from its down-sample image and compute the reconstruction weights, and then use the weights to reconstruct high resolution image patches with the help of the input low resolution image. In our framework, we also apply the Shapley value to define the nearest patches.
Finally, we do several experiments to show that it is well visual quality and the objective criteria, which is represented as the PSNR value.

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